Machine Learning Science Intern

Role overview Join a research-focused machine-learning team for a four-to-six-month, full-time internship exploring how large models can transfer useful capabilities to smaller models. The work combines open-ended investigation with practical prototyping, evaluation, and opportunities to move successful ideas toward production or publication.

Responsibilities - Implement, benchmark, and iterate on methods for knowledge distillation and synthetic-data generation. - Investigate data diversity, validation and filtering, model compression, reasoning, and tool-use performance. - Expand benchmarks covering task quality, latency, memory usage, and different hardware profiles. - Test promising prototypes on realistic workloads and incorporate lessons from engineering feedback. - Collaborate on integrating successful experiments into Python and machine-learning systems. - Present findings, contribute to research discussions, and prepare publishable results where appropriate.

Requirements - Current enrollment in a PhD program in computer science, machine learning, robotics, statistics, mathematics, or a related field. - Strong foundations in machine learning and depth in at least one area such as deep learning, NLP, reinforcement learning, or optimization. - Proficiency in Python and a modern machine-learning framework such as PyTorch, JAX, or TensorFlow. - Ability to turn an open research question into a focused experimental plan.

Nice to have - Research authorship at a workshop, conference, or journal. - Experience with distillation, quantization, pruning, synthetic data, model self-improvement, or language-model research. - Contributions to open-source machine-learning projects or an active technical portfolio.

Benefits and work setup - Dedicated mentorship and access to experienced research and engineering collaborators. - Support for conference participation when work produces publishable results. - Remote work within European time zones, with periodic in-person planning sessions. - Flexible start date and a four-to-six-month full-time duration.